[Early recognition and screening consultation: a necessary way to improve early detection and treatment in rheumatology? : Overview of the early recognition and screening consultation models for rheumatic and musculoskeletal diseases in Germany]

[The subanalysis of Rheuma-VOR demonstrates a considerable need for rheumatological care]

BACKGROUND

Early diagnosis and treatment of inflammatory rheumatic diseases can prevent consequential damage such as permanently limited mobility and joint or organ damage. Simultaneously, there is an increasing deficit in medical care owing to the lack of rheumatological capacity. Rural regions are particularly affected.

OBJECTIVES

The available unconfirmed diagnoses of the study Rheuma-VOR were analysed regarding another definitive inflammatory rheumatic disease.

MATERIALS AND METHODS

The returned questionnaires of the rheumatologists participating in Rheuma-VOR were screened for definitive inflammatory rheumatic diseases other than the required diagnosis of rheumatoid arthritis, psoriatic arthritis or spondyloarthritis.

RESULTS

Of 910 unconfirmed diagnoses, in 245 patients another definitive diagnosis could be confirmed. A total of 29.8% of the diagnoses corresponded to degenerative joint changes or chronic pain syndrome, whereas 26.1% involved different forms of inflammatory arthritis. The majority of diagnoses (40.5%) were collagenosis or vasculitis, DISCUSSION: The available data show that a rheumatological presentation was indicated for the majority of patients. Owing to the increasing deficits in medical care a prior selection of the patients is crucial to make optimal use of restricted rheumatological capacities.

Prioritising Appointments by Telephone Interview: Duration from Symptom Onset to Appointment Request Predicts Likelihood of Inflammatory Rheumatic Disease

Background: This study aims to determine the rate of inflammatory rheumatic diseases (IRDs) in a cohort of initial referrals and the efficacy of prioritising appointments to the early arthritis clinic (EAC) based on symptom duration. Methods: In the present study, we used algorithm-based telephone triage to assign routine care appointments according to the time between symptom onset and request for an appointment (cut-off criterion: 6 months). This retrospective, monocentric analysis evaluated the effectiveness of our triage in identifying patients with IRDs as a function of the assigned appointment category (elective, EAC, or emergency appointment). Results: A total of 1407 patients were included in the study (34.7% male; 65.3% female). Of the 1407 patients evaluated, 361 (25.7%) presented with IRD. There were significant differences in the frequency of inflammatory diagnoses between appointment categories (p < 0.001): elective 13.8%, EAC 32.9%, and emergency 45.9%. The sample without the emergency category included a total of 1222 patients. The classification into "inflammatory" or "non-inflammatory" in this subsample was as follows: Sensitivity was 37.7%, and specificity was 92.6%. The positive predictive value (PPV) was 59.8%, and the negative predictive value (NPV) was 83.6%. Overall, 80.2% of patients were correctly assigned using the appointment category and C-reactive protein (CRP). Conclusions: The algorithm-based triage system presented here, which focuses on the time between symptom onset and request for an appointment, allows for the prioritisation of appointments in favour of patients with IRDs and thus earlier initiation of therapy.

Diagnostic Accuracy of a Mobile AI-Based Symptom Checker and a Web-Based Self-Referral Tool in Rheumatology: Multicenter Randomized Controlled Trial

BACKGROUND

The diagnosis of inflammatory rheumatic diseases (IRDs) is often delayed due to unspecific symptoms and a shortage of rheumatologists. Digital diagnostic decision support systems (DDSSs) have the potential to expedite diagnosis and help patients navigate the health care system more efficiently.

OBJECTIVE

The aim of this study was to assess the diagnostic accuracy of a mobile artificial intelligence (AI)-based symptom checker (Ada) and a web-based self-referral tool (Rheport) regarding IRDs.

METHODS

A prospective, multicenter, open-label, crossover randomized controlled trial was conducted with patients newly presenting to 3 rheumatology centers. Participants were randomly assigned to complete a symptom assessment using either Ada or Rheport. The primary outcome was the correct identification of IRDs by the DDSSs, defined as the presence of any IRD in the list of suggested diagnoses by Ada or achieving a prespecified threshold score with Rheport. The gold standard was the diagnosis made by rheumatologists.

RESULTS

A total of 600 patients were included, among whom 214 (35.7%) were diagnosed with an IRD. Most frequent IRD was rheumatoid arthritis with 69 (11.5%) patients. Rheport's disease suggestion and Ada's top 1 (D1) and top 5 (D5) disease suggestions demonstrated overall diagnostic accuracies of 52%, 63%, and 58%, respectively, for IRDs. Rheport showed a sensitivity of 62% and a specificity of 47% for IRDs. Ada's D1 and D5 disease suggestions showed a sensitivity of 52% and 66%, respectively, and a specificity of 68% and 54%, respectively, concerning IRDs. Ada's diagnostic accuracy regarding individual diagnoses was heterogenous, and Ada performed considerably better in identifying rheumatoid arthritis in comparison to other diagnoses (D1: 42%; D5: 64%). The Cohen κ statistic of Rheport for agreement on any rheumatic disease diagnosis with Ada D1 was 0.15 (95% CI 0.08-0.18) and with Ada D5 was 0.08 (95% CI 0.00-0.16), indicating poor agreement for the presence of any rheumatic disease between the 2 DDSSs.

CONCLUSIONS

To our knowledge, this is the largest comparative DDSS trial with actual use of DDSSs by patients. The diagnostic accuracies of both DDSSs for IRDs were not promising in this high-prevalence patient population. DDSSs may lead to a misuse of scarce health care resources. Our results underscore the need for stringent regulation and drastic improvements to ensure the safety and efficacy of DDSSs.

TRIAL REGISTRATION

German Register of Clinical Trials DRKS00017642; https://drks.de/search/en/trial/DRKS00017642.

Comparison of physician and artificial intelligence-based symptom checker diagnostic accuracy

Symptom checkers are increasingly used to assess new symptoms and navigate the health care system. The aim of this study was to compare the accuracy of an artificial intelligence (AI)-based symptom checker (Ada) and physicians regarding the presence/absence of an inflammatory rheumatic disease (IRD). In this survey study, German-speaking physicians with prior rheumatology working experience were asked to determine IRD presence/absence and suggest diagnoses for 20 different real-world patient vignettes, which included only basic health and symptom-related medical history. IRD detection rate and suggested diagnoses of participants and Ada were compared to the gold standard, the final rheumatologists’ diagnosis, reported on the discharge summary report. A total of 132 vignettes were completed by 33 physicians (mean rheumatology working experience 8.8 (SD 7.1) years). Ada’s diagnostic accuracy (IRD) was significantly higher compared to physicians (70 vs 54%, p = 0.002) according to top diagnosis. Ada listed the correct diagnosis more often compared to physicians (54 vs 32%, p < 0.001) as top diagnosis as well as among the top 3 diagnoses (59 vs 42%, p < 0.001). Work experience was not related to suggesting the correct diagnosis or IRD status. Confined to basic health and symptom-related medical history, the diagnostic accuracy of physicians was lower compared to an AI-based symptom checker. These results highlight the potential of using symptom checkers early during the patient journey and importance of access to complete and sufficient patient information to establish a correct diagnosis.

[Further development of regional early care-Many roads lead to Rome : Developmental stages of four established rheumatological early care concepts in different regions of Germany]

In order to shorten the prognostically relevant waiting time until diagnosis and initiation of appropriate treatment in inflammatory rheumatic diseases, rheumatological centers in many regions across Germany have established and continuously developed specific early care concepts. Evaluated models from Altötting·Burghausen, Berlin Buch, Düsseldorf and Heidelberg and their developmental stages as a response to internal and external challenges are presented in this overview. The transparent publication of the developmental steps and the exchange of experiences aim at promoting new early care concepts in other regions and continuing the joint dialogue for improvement of the early detection and quality of care of inflammatory rheumatic diseases in Germany.

Machine learning-based improvement of an online rheumatology referral and triage system

Introduction

Rheport is an online rheumatology referral system allowing automatic appointment triaging of new rheumatology patient referrals according to the respective probability of an inflammatory rheumatic disease (IRD). Previous research reported that Rheport was well accepted among IRD patients. Its accuracy was, however, limited, currently being based on an expert-based weighted sum score. This study aimed to evaluate whether machine learning (ML) models could improve this limited accuracy.

Materials and methods

Data from a national rheumatology registry (RHADAR) was used to train and test nine different ML models to correctly classify IRD patients. Diagnostic performance was compared of ML models and the current algorithm was compared using the area under the receiver operating curve (AUROC). Feature importance was investigated using shapley additive explanation (SHAP).

Results

A complete data set of 2265 patients was used to train and test ML models. 30.5% of patients were diagnosed with an IRD, 69.3% were female. The diagnostic accuracy of the current Rheport algorithm (AUROC of 0.534) could be improved with all ML models, (AUROC ranging between 0.630 and 0.737). Targeting a sensitivity of 90%, the logistic regression model could double current specificity (17% vs. 33%). Finger joint pain, inflammatory marker levels, psoriasis, symptom duration and female sex were the five most important features of the best performing logistic regression model for IRD classification.

Conclusion

In summary, ML could improve the accuracy of a currently used rheumatology online referral system. Including further laboratory parameters and enabling individual feature importance adaption could increase accuracy and lead to broader usage.

[Digitally supported rheumatological screening consultation : How useful is a questionnaire scoring system (RhePort)?]

Regarding scarce capacities an early detection consultation (EDC) was established to discriminate patients in an outpatient setting with inflammatory from non-inflammatory rheumatic diseases. A total of 500 patients suspected of having a rheumatic disease received an appointment within 2 weeks. They were interviewed with the help of a digital questionnaire (RhePort), briefly physically examined followed by a determination of CRP. The questionnaire answers were scored using an algorithm within RhePort (from 0 = non-inflammatory to 4 = highly probably inflammatory). Likewise, after completion of the EDC, the rheumatologists scored the overall assessment. The RhePort score and EDC score were compared with the "true" diagnosis made in a detailed second examination after an average of 10 weeks. In 490 evaluable patients 133 inflammatory (27%) and 357 noninflammatory rheumatic diseases (73%) were diagnosed. A classification based solely on the RhePort questionnaire (score > 1) identified 103 out of 129 as inflammatory (sens. 80%) and 125 out of 355 as non-inflammatory (spec. 35%) resulting in an AUC of 0.62 after ROC analysis. With a score > 1, the rheumatological assessment after EDC classified 130 out of 133 patients as inflammatory (sensitivity 98%) and 261 out of 357 as non-inflammatory (specificity 73%). The combined EDC can decisively increase the sensitivity and specificity compared to an "automated" survey by means of a digital questionnaire alone. In addition to the early identification and treatment of inflammatory patients, rapid identification of patients who are not in need of rheumatological treatment can create capacities for care.

Patient's Perception of Digital Symptom Assessment Technologies in Rheumatology: Results From a Multicentre Study

Introduction

An increasing number of digital tools, including dedicated diagnostic decision support systems (DDSS) exist to better assess new symptoms and understand when and where to seek medical care. The aim of this study was to evaluate patient's previous online assessment experiences and to compare the acceptability, usability, usefulness and potential impact of artificial intelligence (AI)-based symptom checker (Ada) and an online questionnaire-based self-referral tool (Rheport).

Materials and Methods

Patients newly presenting to three German secondary rheumatology outpatient clinics were randomly assigned in a 1:1 ratio to complete consecutively Ada or Rheport in a prospective non-blinded multicentre controlled crossover randomized trial. DDSS completion time was recorded by local study personnel and perceptions on DDSS and previous online assessment were collected through a self-completed study questionnaire, including usability measured with the validated System Usability Scale (SUS).

Results

600 patients (median age 52 years, 418 women) were included. 277/600 (46.2%) of patients used an online search engine prior to the appointment. The median time patients spent assessing symptoms was 180, 7, and 8 min, respectively using online using search engines, Ada and Rheport. 111/275 (40.4%), 266/600 (44.3%) and 395/600 (65.8%) of patients rated the respective symptom assessment as very helpful or helpful, using online search engines, Ada and Rheport, respectively. Usability of both diagnostic decision support systems (DDSS) was “good” with a significantly higher mean SUS score (SD) of Rheport 77.1/100 (16.0) compared to Ada 74.4/100 (16.8), (p < 0.0001). In male patients, usability of Rheport was rated higher than Ada (p = 0.02) and the usability rating of older (52 years ≥) patients of both DDSS was lower than in younger participants (p = 0.005). Both effects were independent of each other. 440/600 (73.3%) and 475/600 (79.2%) of the patients would recommend Ada and Rheport to friends and other patients, respectively.

Conclusion

In summary, patients increasingly assess their symptoms independently online, however only a minority used dedicated symptom assessment websites or DDSS. DDSS, such as Ada an Rheport are easy to use, well accepted among patients with musculoskeletal complaints and could replace online search engines for patient symptom assessment, potentially saving time and increasing helpfulness.

Rheumatic?-A Digital Diagnostic Decision Support Tool for Individuals Suspecting Rheumatic Diseases: A Multicenter Pilot Validation Study

Introduction

Digital diagnostic decision support tools promise to accelerate diagnosis and increase health care efficiency in rheumatology. Rheumatic? is an online tool developed by specialists in rheumatology and general medicine together with patients and patient organizations. It calculates a risk score for several rheumatic diseases. We ran a pilot study retrospectively testing Rheumatic? for its ability to differentiate symptoms from existing or emerging immune-mediated rheumatic diseases from other rheumatic and musculoskeletal complaints and disorders in patients visiting rheumatology clinics.

Materials and Methods

The performance of Rheumatic? was tested using in three university rheumatology centers: (A) patients at Risk for RA (Karolinska Institutet, n = 50 individuals with musculoskeletal complaints and anti-citrullinated protein antibody positivity) (B) patients with early joint swelling [dataset B (Erlangen) n = 52]. (C) Patients with early arthritis where the clinician considered it likely to be of auto-immune origin [dataset C (Leiden) n = 73]. In dataset A we tested whether Rheumatic? could predict the development of arthritis. In dataset B and C we tested whether Rheumatic? could predict the development of an immune-mediated rheumatic diseases. We examined the discriminative power of the total score with the Wilcoxon rank test and the area-under-the-receiver-operating-characteristic curve (AUC-ROC). Next, we calculated the test characteristics for these patients passing the first or second expert-based Rheumatic? scoring threshold.

Results

The total test scores differentiated between: (A) Individuals developing arthritis or not, median 245 vs. 163, P < 0.0001, AUC-ROC = 75.3; (B) patients with an immune-mediated arthritic disease or not median 191 vs. 107, P < 0.0001, AUC-ROC = 79.0; but less patients with an immune-mediated arthritic disease or not amongst those where the clinician already considered an immune mediated disease most likely (median 262 vs. 212, P < 0.0001, AUC-ROC = 53.6). Threshold-1 (advising to visit primary care doctor) was highly specific in dataset A and B (0.72, 0.87, and 0.23, respectively) and sensitive (0.67, 0.61, and 0.67). Threshold-2 (advising to visit rheumatologic care) was very specific in all three centers but not very sensitive: specificity of 1.0, 0.96, and 0.91, sensitivity 0.05, 0.07, 0.14 in dataset A, B, and C, respectively.

Conclusion

Rheumatic? is a web-based patient-centered multilingual diagnostic tool capable of differentiating immune-mediated rheumatic conditions from other musculoskeletal problems. The current scoring system needs to be further optimized.

[Digital diagnostic support in rheumatology]

Only the correct diagnosis enables an effective treatment of rheumatic diseases. Digitalization has already significantly accelerated and simplified our everyday life. An increasing number of digital options are available to patients and medical personnel in rheumatology to accelerate and improve the diagnosis. This work gives an overview of current developments and tools for patients and rheumatologists, regarding digital diagnostic support in rheumatology.

[Care atlas rheumatology : Approaches and concepts for improving care in outpatient rheumatology]

Inflammatory rheumatic diseases affect 1.5 million adults and an estimated 20,000 children and adolescents throughout Germany. The successful treatment of these patients is largely based on the availability of high-quality medical care. To be able to provide sufficient care and prevent long waiting times even though the number of rheumatologists is below demand, efficient practice structures and approaches that go beyond standard care play an important role. The present study takes a look at the current state of rheumatological outpatient care as well as innovative care initiatives to support the service provision structures and to improve the care situation in rheumatology and points out: to ensure guideline-based care despite scarce resources, selective contracts, integrated outpatient specialist care (ASV), early or emergency consultation hours, disease management programs (DMP) and appropriate delegation of medical services play an important role. New care concepts increasingly focus on interdisciplinary cooperation (DMP and ASV), strengthened self-management through structured patient training (DMP) and targeted patient management through screening tools. To ensure an up to date and high-quality treatment in the long term, an increase in further training in rheumatology is necessary. This should be achieved by attracting more students and, if necessary, adjusting the training system.

Accuracy, patient-perceived usability, and acceptance of two symptom checkers (Ada and Rheport) in rheumatology: interim results from a randomized controlled crossover trial

BACKGROUND

Timely diagnosis and treatment are essential in the effective management of inflammatory rheumatic diseases (IRDs). Symptom checkers (SCs) promise to accelerate diagnosis, reduce misdiagnoses, and guide patients more effectively through the health care system. Although SCs are increasingly used, there exists little supporting evidence.

OBJECTIVE

To assess the diagnostic accuracy, patient-perceived usability, and acceptance of two SCs: (1) Ada and (2) Rheport.

METHODS

Patients newly presenting to a German secondary rheumatology outpatient clinic were randomly assigned in a 1:1 ratio to complete Ada or Rheport and consecutively the respective other SCs in a prospective non-blinded controlled randomized crossover trial. The primary outcome was the accuracy of the SCs regarding the diagnosis of an IRD compared to the physicians' diagnosis as the gold standard. The secondary outcomes were patient-perceived usability, acceptance, and time to complete the SC.

RESULTS

In this interim analysis, the first 164 patients who completed the study were analyzed. 32.9% (54/164) of the study subjects were diagnosed with an IRD. Rheport showed a sensitivity of 53.7% and a specificity of 51.8% for IRDs. Ada's top 1 (D1) and top 5 disease suggestions (D5) showed a sensitivity of 42.6% and 53.7% and a specificity of 63.6% and 54.5% concerning IRDs, respectively. The correct diagnosis of the IRD patients was within the Ada D1 and D5 suggestions in 16.7% (9/54) and 25.9% (14/54), respectively. The median System Usability Scale (SUS) score of Ada and Rheport was 75.0/100 and 77.5/100, respectively. The median completion time for both Ada and Rheport was 7.0 and 8.5 min, respectively. Sixty-four percent and 67.1% would recommend using Ada and Rheport to friends and other patients, respectively.

CONCLUSIONS

While SCs are well accepted among patients, their diagnostic accuracy is limited to date.

TRIAL REGISTRATION

DRKS.de, DRKS00017642 . Registered on 23 July 2019.

[Regional cooperative rheumatology centers : Networking of rheumatology in Germany for over 25 years]

Over the last 25 years the regional collaborative rheumatology centers have become established throughout Germany as "catalysts" for improving rheumatological care at many different levels. With campaigns and a multitude of activities they have promoted the visibility of rheumatology in Germany together with rheumatological alliance partners and, on the scientific side, together with the German Rheumatism Research Center have contributed to the improvement of rheumatological care research in Germany. The regional cooperative rheumatology centers have become an important partner in the rheumatology network in their association as a working group of the German Society for Rheumatology and they will continue to face new tasks in the future to further improve rheumatological care in Germany.

[Current state, goals and quality standards of outpatient care in rheumatology: position paper of the Professional Association of German Rheumatologists (BDRh)]

Even in the era of modern guidelines, the treatment of rheumatic diseases is only as good as the framework of rheumatological care within which the treatment is carried out. The access to high-quality medical treatment for all patients is therefore essentially decisive for the prognosis of the patients. This article describes the current state of outpatient treatment in rheumatology and demonstrates which quality projects, such as treatment contracts, outpatient specialized medical treatment (ASV), digitalization and training as specialized rheumatological assistant (RFA), have been created in order to ensure the treatment of our patients. Furthermore, standards are defined that can guarantee a contemporary and guideline-conform treatment in outpatient rheumatological units. As an example it is an affirmation of the Professional Association of German Rheumatologists (BDRh) for ensuring optimal care for all rheumatology patients through early or emergency rheumatology clinics, treat to target, appropriate delegation of medical duties and diversification of treatment, thus an assurance of the quality and comprehensive treatment in rheumatology. The important topic of safeguarding the next generation of rheumatologists, which is indispensable for this, is also discussed.

[Position paper of the commission on digital rheumatology of the German Society of Rheumatology: tasks, targets and perspectives for a modern rheumatology]

Digitalization in the healthcare system is a great challenge for rheumatology as for other medical disciplines. The German Society for Rheumatology (DGRh) wants to actively participate in this process and benefit from it. By founding the commission on digital rheumatology, the DGRh has created a committee that deals with the associated tasks, advises the DGRh on questions and positions associated with digital health. For the DGRh, this affects the most diverse areas of digitalization in medicine and rheumatology. This position paper presents the topics and developments currently handled by the commission and the tasks identified.

Toward Earlier Diagnosis Using Combined eHealth Tools in Rheumatology: The Joint Pain Assessment Scoring Tool (JPAST) Project

Outcomes of patients with inflammatory rheumatic diseases have significantly improved over the last three decades, mainly due to therapeutic innovations, more timely treatment, and a recognition of the need to monitor response to treatment and to titrate treatments accordingly. Diagnostic delay remains a major challenge for all stakeholders. The combination of electronic health (eHealth) and serologic and genetic markers holds great promise to improve the current management of patients with inflammatory rheumatic diseases by speeding up access to appropriate care. The Joint Pain Assessment Scoring Tool (JPAST) project, funded by the European Union (EU) European Institute of Innovation and Technology (EIT) Health program, is a unique European project aiming to enable and accelerate personalized precision medicine for early treatment in rheumatology, ultimately also enabling prevention. The aim of the project is to facilitate these goals while at the same time, reducing cost for society and patients.